Agentic readability asks whether an agent can correctly identify, understand, navigate and use a digital presence. It concerns properties the organization can design, test and improve.
Agentic discoverability asks whether that presence then enters an agent’s field of consideration for a given intent, context and time, and whether it is selected or recommended.
Readability is therefore an upstream condition, not a result guarantee.
Why does the distinction matter?
Without this separation, an organization may confuse three findings:
- its website exposes resources correctly;
- a system understands the organization correctly;
- the system actually chooses the organization in an answer or action.
These findings require different evidence. A website may be highly readable without being considered for a particular query. A brand may also be recommended frequently while being represented inaccurately.
What can Pagup measure?
Pagup can verify controllable conditions: structure, identity, evidence, routes, structured data, machine surfaces, interfaces, states and boundaries. Pagup can also observe external outputs through versioned scenarios.
Pagup cannot assert an internal rank or candidate set when the system provides no trace. In that case, the internal step remains unevaluated.
The positioning page explains how these concepts fit into the digital readability and agentic readiness model. The agentic discoverability glossary entry presents the full professional application.
The conceptual definition and versioned framework are published by Gautier Dorval: